Algorithms are the sets of steps necessary to complete computation - they are at the heart of what our devices actually do. And this isn’t a new concept. Since the development of math itself algorithms have been needed to help us complete tasks more efficiently, but today we’re going to take a look a couple modern computing problems like sorting and graph search, and show how we’ve made them more efficient so you can more easily find cheap airfare or map directions to Winterfell... or like a restaurant or something.
Ps. Have you had the chance to play the Grace Hopper game we made in episode 12. Check it out here! http://thoughtcafe.ca/hopper/
CORRECTION:
In the pseudocode for selection sort at 3:09, this line:
swap array items at index and smallest
should be:
swap array items at i and smallest
Produced in collaboration with PBS Digital Studios: http://youtube.com/pbsdigitalstudios
Want to know more about Carrie Anne?
https://about.me/carrieannephilbin
The Latest from PBS Digital Studios: https://www.youtube.com/playlist?list...
Want to find Crash Course elsewhere on the internet?
Facebook - https://www.facebook.com/YouTubeCrash...
Twitter - http://www.twitter.com/TheCrashCourse
Tumblr - http://thecrashcourse.tumblr.com
Support Crash Course on Patreon: http://patreon.com/crashcourse
CC Kids: http://www.youtube.com/crashcoursekids

published:24 May 2017

views:560881

How to calculate the order/efficiency/run-time of an algorithm and why these are important. www.hegartymaths.com http://www.hegartymaths.com/

published:15 Feb 2012

views:26560

This video is part of an online course, Intro to Algorithms. Check out the course here: https://www.udacity.com/course/cs215.

Efficiency and TimeComplexity is a very important topic when it comes to programming. You always want to implement the fastest algorithm that handles the task at hand. In today's lesson, let's discuss the differences between O(1) vs O(N), in other words Constant Time vs Linear Time.
Podcasts Course
https://www.letsbuildthatapp.com/course/Podcasts
Intermediate TrainingCoreData
https://www.letsbuildthatapp.com/course/intermediate-training-core-data
Instagram Firebase Course
https://www.letsbuildthatapp.com/course/instagram-firebase
FacebookGroup
https://www.facebook.com/groups/1240636442694543/
Completed Source Code
https://www.letsbuildthatapp.com/course_video?id=2852
Instagram: https://www.instagram.com/buildthatapp/
Twitter: https://twitter.com/buildthatapp

published:13 Feb 2018

views:9879

http://www.cppcon.org
—
Presentation Slides, PDFs, Source Code and other presenter materials are available at: https://github.com/CppCon/CppCon2014
--
Why do you write C++ code? There is a good chance it is in part because of concerns about the performance of your software. Whether they stem from needing to run on every smaller mobile devices, squeezing the last few effects into video game, or because every watt of power in your data center costs too much, C++ programmers throughout the industry have an insatiable desire for writing high performance code.
Unfortunately, even with C++, this can be really challenging. Over the past twenty years processors, memory, software libraries, and even compilers have radically changed what makes C++ code fast. Even measuring the performance of your code can be a daunting task. This talk will dig into how modern processors work, what makes them fast, and how to exploit them effectively with modern C++ code. It will teach you how modern C++ optimizers see your code today, and how that is likely to change in the coming years. It will teach you how to reason better about the performance of your code, and how to write your code so that it performs better. You will even learn some tricks about how to measure the performance of your code.
--
Chandler Carruth leads the Clang team at Google, building better diagnostics, tools, and more. Previously, he worked on several pieces of Google’s distributed build system. He makes guest appearances helping to maintain a few core C++ libraries across Google’s codebase, and is active in the LLVM and Clang open source communities. He received his M.S. and B.S. in Computer Science from Wake Forest University, but disavows all knowledge of the contents of his Master’s thesis. He is regularly found drinking Cherry CokeZero in the daytime and pontificating over a single malt scotch in the evening.
--
Videos Filmed & Edited by Bash Films: http://www.BashFilms.com

published:21 Oct 2014

views:93517

What is the fastest sorting algorithm known to man? We find out by attempting to find an O(n) time complexity sorting algorithm. Till now, the research on sorting has found O(n*log(n)) to be the best.
We discuss concepts of time complexity, decision making and how the best algorithm is bounded by the permutation tree of an array. A final trick is used to bring down the complexity to O(N)!
N! Approximation: https://en.wikipedia.org/wiki/Stirling%27s_approximation
log(N) video: https://www.youtube.com/watch?v=Xe9aq1WLpjU&vl=en
Sources:
https://hackernoon.com/timsort-the-fastest-sorting-algorithm-youve-never-heard-of-36b28417f399
https://stackoverflow.com/questions/3969813/which-parallel-sorting-algorithm-has-the-best-average-case-performance
https://stackoverflow.com/questions/2352313/is-there-an-on-integer-sorting-algorithm
#sorting #algorithm #sortingalgorithm

published:24 Aug 2018

views:35229

Count Min sketch is a simple technique to summarize large amounts of frequency data. which is widely used in many places where there is a streaming big data.
Donate/Patreon: https://www.patreon.com/techdummies
CODE:
----------------------------------------------------------------------------
By Varun Vats: https://gist.github.com/VarunVats9/7f379199d7658b96d479ee3c945f1b4a
Applications of count min sketch:
----------------------------------------------------------------------------
http://theory.stanford.edu/~tim/s15/l/l2.pdf
http://highscalability.com/blog/2016/1/25/design-of-a-modern-cache.html
https://spark.apache.org/docs/2.0.1/api/java/org/apache/spark/util/sketch/CountMinSketch.html
Applications using CountTracking There are dozens of applications of count tracking and in particular, the Count-Min sketch datastructure that goes beyond the task of approximating data distributions. We give three examples.
1. A more general query is to identify the Heavy-Hitters, that is, the query HH(k) returns theset of items which have large frequency (say 1/k of the overall frequency). Count trackingcan be used to directly answer this query, by considering the frequency of each item. Whenthere are very many possible items, answering the query in this way can be quite slow. Theprocess can be sped up immensely by keeping additional information about the frequenciesof groups of items [6], at the expense of storing additional sketches. As well as being ofinterest in mining applications, finding heavy-hitters is also of interest in the context of signalprocessing. Here, viewing the signal as defining a data distribution, recovering the heavy-hitters is key to building the best approximation of the signal. As a result, the Count-Minsketch can be used in compressed sensing, a signal acquisition paradigm that has recentlyrevolutionized signal processing [7].
2. One application where very large data sets arise is in Natural Language Processing (NLP).Here, it is important to keep statistics on the frequency of word combinations, such as pairsor triplets of words that occur in sequence. In one experiment, researchers compacted a large6
Page 7
90GB corpus down to a (memory friendly) 8GB Count-Min sketch [8]. This proved to be justas effective for their word similarity tasks as using the exact data.
3. A third example is in designing a mechanism to help users pick a safe password. To makepassword guessing difficult, we can track the frequency of passwords online and disallowcurrently popular ones. This is precisely the count tracking problem. Recently, this wasput into practice using the Count-Min data structure to do count tracking (see http://www.youtube.com/watch?v=qo1cOJFEF0U). A nice feature of this solution is that the impactof a false positive—erroneously declaring a rare password choice to be too popular and sodisallowing it—is only a mild inconvenience to the user

published:30 Sep 2018

views:2584

Learn about Big O notation, an equation that describes how the run time scales with respect to some input variables. This video is a part of HackerRank's Cracking The Coding Interview Tutorial with Gayle Laakmann McDowell. http://www.hackerrank.com/domains/tutorials/cracking-the-coding-interview?utm_source=video&utm_medium=youtube&utm_campaign=ctci

C Programming Tutorial #26 What is an algorithm? Fast arithmetic, Correctness and efficiency, Repeated squaring method. This lecture will introduce the notion of an algorithm and introduce the ideas of correctness and efficiency. The running example is the problem of raising a number to a power, i.e. computing AN for numbers A and N. Two different methods will be discussed and compared. You will see that both methods are correct, but one method is superior to or more efficient than the other. You will learn to quantify the efficiency of the two methods exactly and compare them.
The lecture contains the following topics:
00:20 What is an algorithm?
03:30 Fast arithmetic: Raise a number to a power
10:20 Correctness and efficiency
12:09 Repeated squaring method
C Programming Tutorial/ Lectures/ Course with Data Structures & Algorithms @ https://goo.gl/K6VbgL
Machine LearningComplete Tutorial/Lectures/Course from IIT (nptel) for GATE @ https://goo.gl/AurRXm
Discrete Mathematics for Computer Science for GATE @ https://goo.gl/YJnA4B (IIT Lectures for GATE)
Best Programming Courses @ https://goo.gl/MVVDXR
Operating Systems Lecture/Tutorials from IIT for GATE CSE @ https://goo.gl/GMr3if
MATLAB Tutorials for GATE @ https://goo.gl/EiPgCF

published:11 Jan 2018

views:290

How to Write Algorithm
How to AnalyseSimple Algorithm.
Criteria for Analysing Algorithm
Buy C++ course on Udemy.com
Price: $10.99 (₹750)
URL : https://www.udemy.com/cpp-deep-dive/?couponCode=LEARNCPP
Course covers All the topics from Basics to Advance level.
Every topic is covered in greater detail with suitable examples.
Suitable for Academics and Industry

Efficient (horse)

Efficient was by the outstanding sire Zabeel from the mare Refused The Dance by Defensive Play (USA). He is a half brother to Guillotine, winner of the 2008 MVRC Dato Tan Chin Nam Stakes.

In his first campaign in the spring of 2006 he put together five consecutive wins including the Group 2 Aami Vase at Moonee Valley and ending with a win in the VRC Derby by 2½ lengths. Following his Derby win Efficient was entered into the Melbourne Cup field, run three days later. However, he did not pull up well enough and so was scratched from the race and spelled instead.

Returning in the autumn Efficient had a very light campaign of just two starts. In the 1,400 metre Group 3 Schweppervescence Cup he finished fourth behind Haradasun and fifth behind Miss Finland in the Group OneAustralian Guineas over 1,600 metres.

In relation to logic-based and artificial neural network-based clinical decision support system, which are also computer applications to the medical decision making field, algorithms are less complex in architecture, data structure and user interface. Medical algorithms are not necessarily implemented using digital computers. In fact, many of them can be represented on paper, in the form of diagrams, nomographs, etc.

Algorithm (My Heart to Fear album)

Critical reception

Awarding the album three stars from Alternative Press, Jason Schreurs writes, "As is the case with the bulk of this musical style, the vocals bring it back down to a near-mediocre level." Bradley Zorgdrager, rating the album a five out of ten for Exclaim!, says, "Unfortunately, a lack of inspiration causes the songs to come undone, as many of the parts sound only like a means to get to the next." Giving the album four stars at About.com, Todd Lyons states, "everything binds together into one masterful meditation." Tim Dodderidge, indicating in a 8.5 out of ten review by Mind Equals Blown, writes, "From start to finish, My Heart to Fear’s debut full-length is an energetic, ferocious, cathartic and inspiring metal album."

Kevin Hoskins, giving the album three and a half stars for Jesus Freak Hideout, describes, "this is just metal done well ... but any hardcore fan will be digging this release all summer long." Awarding the album four and a half stars from HM Magazine, Sean Huncherick states, "One good thing about Algorithm is that the band realizes they don’t need to constantly play as fast as they can." Brody B., rating the album four star at Indie Vision Music, writes, "With a few minor tweaks here and there that could have made songs feel more fleshed out I would have had a hard time finding fault with this debut record."

Computer science

Computer science is the scientific and practical approach to computation and its applications. It is the systematic study of the feasibility, structure, expression, and mechanization of the methodical procedures (or algorithms) that underlie the acquisition, representation, processing, storage, communication of, and access to information. An alternate, more succinct definition of computer science is the study of automating algorithmic processes that scale. A computer scientist specializes in the theory of computation and the design of computational systems.

Information technology

Information technology (IT) is the application of computers and telecommunications equipment to store, retrieve, transmit and manipulate data, often in the context of a business or other enterprise. IT is considered a subset of information and communications technology (ICT). In 2012, Zuppo proposed an ICT hierarchy where each hierarchy level "contain some degree of commonality in that they are related to technologies that facilitate the transfer of information and various types of electronically mediated communications.". Business/IT was one level of the ICT hierarchy.

Humans have been storing, retrieving, manipulating and communicating information since the Sumerians in Mesopotamia developed writing in about 3000BC, but the term information technology in its modern sense first appeared in a 1958 article published in the Harvard Business Review; authors Harold J. Leavitt and Thomas L. Whisler commented that "the new technology does not yet have a single established name. We shall call it information technology (IT)." Their definition consists of three categories: techniques for processing, the application of statistical and mathematical methods to decision-making, and the simulation of higher-order thinking through computer programs.

Computer Science (UIL)

Computer Science is designed to test students' programming abilities. It is not the same as the Computer Applications contest, which tests students' abilities to use word processing, spreadsheet, and database applications software, including integration of applications.

Computer Science began during the 1990-91 scholastic year as strictly a team event. It was not scored as an individual event until the 1996-97 school year.

Eligibility

Each school may send up to four students. However, in districts with eight or more schools the number of students per school may be limited to three. In order for the school to compete in the team competition the school must send three students.

Rules and Scoring

The contest consists of two parts, a written test and a programming exercise.

On the written test, 45 minutes are allotted. No time warnings are given, but at the end of the 45 minutes the student may finish completing an answer. Six points are given for each correct answer; two points are deducted for each incorrect answer. Skipped or unanswered questions are not scored.

Intro to Algorithms: Crash Course Computer Science #13

Algorithms are the sets of steps necessary to complete computation - they are at the heart of what our devices actually do. And this isn’t a new concept. Since the development of math itself algorithms have been needed to help us complete tasks more efficiently, but today we’re going to take a look a couple modern computing problems like sorting and graph search, and show how we’ve made them more efficient so you can more easily find cheap airfare or map directions to Winterfell... or like a restaurant or something.
Ps. Have you had the chance to play the Grace Hopper game we made in episode 12. Check it out here! http://thoughtcafe.ca/hopper/
CORRECTION:
In the pseudocode for selection sort at 3:09, this line:
swap array items at index and smallest
should be:
swap array items at i and smallest
Produced in collaboration with PBS Digital Studios: http://youtube.com/pbsdigitalstudios
Want to know more about Carrie Anne?
https://about.me/carrieannephilbin
The Latest from PBS Digital Studios: https://www.youtube.com/playlist?list...
Want to find Crash Course elsewhere on the internet?
Facebook - https://www.facebook.com/YouTubeCrash...
Twitter - http://www.twitter.com/TheCrashCourse
Tumblr - http://thecrashcourse.tumblr.com
Support Crash Course on Patreon: http://patreon.com/crashcourse
CC Kids: http://www.youtube.com/crashcoursekids

10:51

Order/Efficiency/Run-time of an algorithm (Decision Maths 1)

Order/Efficiency/Run-time of an algorithm (Decision Maths 1)

Order/Efficiency/Run-time of an algorithm (Decision Maths 1)

How to calculate the order/efficiency/run-time of an algorithm and why these are important. www.hegartymaths.com http://www.hegartymaths.com/

1:19

Efficient Algorithms - Intro to Algorithms

Efficient Algorithms - Intro to Algorithms

Efficient Algorithms - Intro to Algorithms

This video is part of an online course, Intro to Algorithms. Check out the course here: https://www.udacity.com/course/cs215.

http://www.cppcon.org
—
Presentation Slides, PDFs, Source Code and other presenter materials are available at: https://github.com/CppCon/CppCon2014
--
Why do you write C++ code? There is a good chance it is in part because of concerns about the performance of your software. Whether they stem from needing to run on every smaller mobile devices, squeezing the last few effects into video game, or because every watt of power in your data center costs too much, C++ programmers throughout the industry have an insatiable desire for writing high performance code.
Unfortunately, even with C++, this can be really challenging. Over the past twenty years processors, memory, software libraries, and even compilers have radically changed what makes C++ code fast. Even measuring the performance of your code can be a daunting task. This talk will dig into how modern processors work, what makes them fast, and how to exploit them effectively with modern C++ code. It will teach you how modern C++ optimizers see your code today, and how that is likely to change in the coming years. It will teach you how to reason better about the performance of your code, and how to write your code so that it performs better. You will even learn some tricks about how to measure the performance of your code.
--
Chandler Carruth leads the Clang team at Google, building better diagnostics, tools, and more. Previously, he worked on several pieces of Google’s distributed build system. He makes guest appearances helping to maintain a few core C++ libraries across Google’s codebase, and is active in the LLVM and Clang open source communities. He received his M.S. and B.S. in Computer Science from Wake Forest University, but disavows all knowledge of the contents of his Master’s thesis. He is regularly found drinking Cherry CokeZero in the daytime and pontificating over a single malt scotch in the evening.
--
Videos Filmed & Edited by Bash Films: http://www.BashFilms.com

9:41

Fastest Sorting Algorithm. Ever!

Fastest Sorting Algorithm. Ever!

Fastest Sorting Algorithm. Ever!

What is the fastest sorting algorithm known to man? We find out by attempting to find an O(n) time complexity sorting algorithm. Till now, the research on sorting has found O(n*log(n)) to be the best.
We discuss concepts of time complexity, decision making and how the best algorithm is bounded by the permutation tree of an array. A final trick is used to bring down the complexity to O(N)!
N! Approximation: https://en.wikipedia.org/wiki/Stirling%27s_approximation
log(N) video: https://www.youtube.com/watch?v=Xe9aq1WLpjU&vl=en
Sources:
https://hackernoon.com/timsort-the-fastest-sorting-algorithm-youve-never-heard-of-36b28417f399
https://stackoverflow.com/questions/3969813/which-parallel-sorting-algorithm-has-the-best-average-case-performance
https://stackoverflow.com/questions/2352313/is-there-an-on-integer-sorting-algorithm
#sorting #algorithm #sortingalgorithm

Count Min sketch is a simple technique to summarize large amounts of frequency data. which is widely used in many places where there is a streaming big data.
Donate/Patreon: https://www.patreon.com/techdummies
CODE:
----------------------------------------------------------------------------
By Varun Vats: https://gist.github.com/VarunVats9/7f379199d7658b96d479ee3c945f1b4a
Applications of count min sketch:
----------------------------------------------------------------------------
http://theory.stanford.edu/~tim/s15/l/l2.pdf
http://highscalability.com/blog/2016/1/25/design-of-a-modern-cache.html
https://spark.apache.org/docs/2.0.1/api/java/org/apache/spark/util/sketch/CountMinSketch.html
Applications using CountTracking There are dozens of applications of count tracking and in particular, the Count-Min sketch datastructure that goes beyond the task of approximating data distributions. We give three examples.
1. A more general query is to identify the Heavy-Hitters, that is, the query HH(k) returns theset of items which have large frequency (say 1/k of the overall frequency). Count trackingcan be used to directly answer this query, by considering the frequency of each item. Whenthere are very many possible items, answering the query in this way can be quite slow. Theprocess can be sped up immensely by keeping additional information about the frequenciesof groups of items [6], at the expense of storing additional sketches. As well as being ofinterest in mining applications, finding heavy-hitters is also of interest in the context of signalprocessing. Here, viewing the signal as defining a data distribution, recovering the heavy-hitters is key to building the best approximation of the signal. As a result, the Count-Minsketch can be used in compressed sensing, a signal acquisition paradigm that has recentlyrevolutionized signal processing [7].
2. One application where very large data sets arise is in Natural Language Processing (NLP).Here, it is important to keep statistics on the frequency of word combinations, such as pairsor triplets of words that occur in sequence. In one experiment, researchers compacted a large6
Page 7
90GB corpus down to a (memory friendly) 8GB Count-Min sketch [8]. This proved to be justas effective for their word similarity tasks as using the exact data.
3. A third example is in designing a mechanism to help users pick a safe password. To makepassword guessing difficult, we can track the frequency of passwords online and disallowcurrently popular ones. This is precisely the count tracking problem. Recently, this wasput into practice using the Count-Min data structure to do count tracking (see http://www.youtube.com/watch?v=qo1cOJFEF0U). A nice feature of this solution is that the impactof a false positive—erroneously declaring a rare password choice to be too popular and sodisallowing it—is only a mild inconvenience to the user

8:37

Big O Notation

Big O Notation

Big O Notation

Learn about Big O notation, an equation that describes how the run time scales with respect to some input variables. This video is a part of HackerRank's Cracking The Coding Interview Tutorial with Gayle Laakmann McDowell. http://www.hackerrank.com/domains/tutorials/cracking-the-coding-interview?utm_source=video&utm_medium=youtube&utm_campaign=ctci

5:10

How to manage your time more effectively (according to machines) - Brian Christian

How to manage your time more effectively (according to machines) - Brian Christian

How to manage your time more effectively (according to machines) - Brian Christian

#26 What is an algorithm? Its Correctness and efficiency

C Programming Tutorial #26 What is an algorithm? Fast arithmetic, Correctness and efficiency, Repeated squaring method. This lecture will introduce the notion of an algorithm and introduce the ideas of correctness and efficiency. The running example is the problem of raising a number to a power, i.e. computing AN for numbers A and N. Two different methods will be discussed and compared. You will see that both methods are correct, but one method is superior to or more efficient than the other. You will learn to quantify the efficiency of the two methods exactly and compare them.
The lecture contains the following topics:
00:20 What is an algorithm?
03:30 Fast arithmetic: Raise a number to a power
10:20 Correctness and efficiency
12:09 Repeated squaring method
C Programming Tutorial/ Lectures/ Course with Data Structures & Algorithms @ https://goo.gl/K6VbgL
Machine LearningComplete Tutorial/Lectures/Course from IIT (nptel) for GATE @ https://goo.gl/AurRXm
Discrete Mathematics for Computer Science for GATE @ https://goo.gl/YJnA4B (IIT Lectures for GATE)
Best Programming Courses @ https://goo.gl/MVVDXR
Operating Systems Lecture/Tutorials from IIT for GATE CSE @ https://goo.gl/GMr3if
MATLAB Tutorials for GATE @ https://goo.gl/EiPgCF

10:37

1.3 How Write and Analyze Algorithm

1.3 How Write and Analyze Algorithm

1.3 How Write and Analyze Algorithm

How to Write Algorithm
How to AnalyseSimple Algorithm.
Criteria for Analysing Algorithm
Buy C++ course on Udemy.com
Price: $10.99 (₹750)
URL : https://www.udemy.com/cpp-deep-dive/?couponCode=LEARNCPP
Course covers All the topics from Basics to Advance level.
Every topic is covered in greater detail with suitable examples.
Suitable for Academics and Industry

30:05

Largest Area under Histogram efficient algorithm using Stack | Coding Interview Question

Largest Area under Histogram efficient algorithm using Stack | Coding Interview Question

Largest Area under Histogram efficient algorithm using Stack | Coding Interview Question

How to find out the largest area under Histogram in a Stack ? Learn to solve in this tutorial :)
For more online courses visit - http://www.codingblocks.com
Check courses on - http://online.codingblocks.com [FreeTrialAvailable]
Coding Blocks is pleased to announce courses like C++ and Java, Data Structures and Algorithms, Web and AndroidDevelopment(Java and Kotlin), Competitive Programming, Coding Interview Preparation and Machine Learning, AI and more.
#CodingBlocks #ProgrammingMadeEasy #LearnCodingOnline
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For more interesting tutorials - https://www.youtube.com/channel/UCICWIYEx2mo4wYZzLwJ7wVw

3:11

An efficient bandit algorithm for realtime multivariate optimization

An efficient bandit algorithm for realtime multivariate optimization

An efficient bandit algorithm for realtime multivariate optimization

An efficient bandit algorithm for realtime multivariate optimization
Daniel Hill (Amazon.com)
Houssam Nassif (Amazon.com)
Yi Liu (Amazon.com)
Anand Iyer (Amazon.com)
S. V. N. Vishwanathan (vishy@amazon.com)
Optimization is commonly employed to determine the content of web pages, such as to maximize conversions on landing pages or click-through rates on search engine result pages. Often the layout of these pages can be decoupled into several separate decisions. For example, the composition of a landing page may involve deciding which image to show, which wording to use, what color background to display, etc. Thus, optimization is a combinatorial problem over an exponentially large decision space. Randomized experiments do not scale well to this setting, and therefore, in practice, one is typically limited to optimizing a single aspect of a web page at a time. This represents a missed opportunity in both the speed of experimentation and the exploitation of possible interactions between layout decisions.
Here we focus on multivariate optimization of interactive web pages. We formulate an approach where the possible interactions between different components of the page are modeled explicitly. We apply bandit methodology to explore the layout space efficiently and use hill-climbing to select optimal content in realtime. Our algorithm also extends to contextualization and personalization of layout selection. Simulation results show the suitability of our approach to large decision spaces with strong interactions between content. We further apply our algorithm to optimize a message that promotes adoption of an Amazon service. After only a single week of online optimization, we saw a 21\% increase in purchase rate compared to the average layout. Our technique is currently being deployed to optimize content across several locations at Amazon.com.
More on http://www.kdd.org/kdd2017/

Intro to Algorithms: Crash Course Computer Science #13

Algorithms are the sets of steps necessary to complete computation - they are at the heart of what our devices actually do. And this isn’t a new concept. Since the development of math itself algorithms have been needed to help us complete tasks more efficiently, but today we’re going to take a look a couple modern computing problems like sorting and graph search, and show how we’ve made them more efficient so you can more easily find cheap airfare or map directions to Winterfell... or like a restaurant or something.
Ps. Have you had the chance to play the Grace Hopper game we made in episode 12. Check it out here! http://thoughtcafe.ca/hopper/
CORRECTION:
In the pseudocode for selection sort at 3:09, this line:
swap array items at index and smallest
should be:
swap array items at i...

published: 24 May 2017

Order/Efficiency/Run-time of an algorithm (Decision Maths 1)

How to calculate the order/efficiency/run-time of an algorithm and why these are important. www.hegartymaths.com http://www.hegartymaths.com/

published: 15 Feb 2012

Efficient Algorithms - Intro to Algorithms

This video is part of an online course, Intro to Algorithms. Check out the course here: https://www.udacity.com/course/cs215.

http://www.cppcon.org
—
Presentation Slides, PDFs, Source Code and other presenter materials are available at: https://github.com/CppCon/CppCon2014
--
Why do you write C++ code? There is a good chance it is in part because of concerns about the performance of your software. Whether they stem from needing to run on every smaller mobile devices, squeezing the last few effects into video game, or because every watt of power in your data center costs too much, C++ programmers throughout the industry have an insatiable desire for writing high performance code.
Unfortunately, even with C++, this can be really challenging. Over the past twenty years processors, memory, software libraries, and even compilers have radically changed what makes C++ code fast. Even measuring the performance of your c...

published: 21 Oct 2014

Fastest Sorting Algorithm. Ever!

What is the fastest sorting algorithm known to man? We find out by attempting to find an O(n) time complexity sorting algorithm. Till now, the research on sorting has found O(n*log(n)) to be the best.
We discuss concepts of time complexity, decision making and how the best algorithm is bounded by the permutation tree of an array. A final trick is used to bring down the complexity to O(N)!
N! Approximation: https://en.wikipedia.org/wiki/Stirling%27s_approximation
log(N) video: https://www.youtube.com/watch?v=Xe9aq1WLpjU&vl=en
Sources:
https://hackernoon.com/timsort-the-fastest-sorting-algorithm-youve-never-heard-of-36b28417f399
https://stackoverflow.com/questions/3969813/which-parallel-sorting-algorithm-has-the-best-average-case-performance
https://stackoverflow.com/questions/2352313/is...

Count Min sketch is a simple technique to summarize large amounts of frequency data. which is widely used in many places where there is a streaming big data.
Donate/Patreon: https://www.patreon.com/techdummies
CODE:
----------------------------------------------------------------------------
By Varun Vats: https://gist.github.com/VarunVats9/7f379199d7658b96d479ee3c945f1b4a
Applications of count min sketch:
----------------------------------------------------------------------------
http://theory.stanford.edu/~tim/s15/l/l2.pdf
http://highscalability.com/blog/2016/1/25/design-of-a-modern-cache.html
https://spark.apache.org/docs/2.0.1/api/java/org/apache/spark/util/sketch/CountMinSketch.html
Applications using CountTracking There are dozens of applications of count tracking and in parti...

published: 30 Sep 2018

Big O Notation

Learn about Big O notation, an equation that describes how the run time scales with respect to some input variables. This video is a part of HackerRank's Cracking The Coding Interview Tutorial with Gayle Laakmann McDowell. http://www.hackerrank.com/domains/tutorials/cracking-the-coding-interview?utm_source=video&utm_medium=youtube&utm_campaign=ctci

published: 27 Sep 2016

How to manage your time more effectively (according to machines) - Brian Christian

Download a free audiobook and support TED-Ed's nonprofit mission: http://adbl.co/2lFSkUw
Check out Brian Christian and Tom Griffiths' "Algorithms to Live By": http://bit.ly/2CadQrt
View full lesson: https://ed.ted.com/lessons/how-to-manage-your-time-more-effectively-according-to-machines-brian-christian
Human beings and computers alike share the challenge of how to get as much done as possible in a limited time. Over the last fifty or so years, computer scientists have learned a lot of good strategies for managing time effectively — and they have a lot of experience with what can go wrong. Brian Christian shares how we can use some of these insights to help make the most of our own lives.
Lesson by Brian Christian, animation by AdriaticAnimation.
Thank you so much to our patrons for ...

published: 02 Jan 2018

#26 What is an algorithm? Its Correctness and efficiency

C Programming Tutorial #26 What is an algorithm? Fast arithmetic, Correctness and efficiency, Repeated squaring method. This lecture will introduce the notion of an algorithm and introduce the ideas of correctness and efficiency. The running example is the problem of raising a number to a power, i.e. computing AN for numbers A and N. Two different methods will be discussed and compared. You will see that both methods are correct, but one method is superior to or more efficient than the other. You will learn to quantify the efficiency of the two methods exactly and compare them.
The lecture contains the following topics:
00:20 What is an algorithm?
03:30 Fast arithmetic: Raise a number to a power
10:20 Correctness and efficiency
12:09 Repeated squaring method
C Programming Tutorial/ Lecture...

published: 11 Jan 2018

1.3 How Write and Analyze Algorithm

How to Write Algorithm
How to AnalyseSimple Algorithm.
Criteria for Analysing Algorithm
Buy C++ course on Udemy.com
Price: $10.99 (₹750)
URL : https://www.udemy.com/cpp-deep-dive/?couponCode=LEARNCPP
Course covers All the topics from Basics to Advance level.
Every topic is covered in greater detail with suitable examples.
Suitable for Academics and Industry

published: 18 Jan 2018

Largest Area under Histogram efficient algorithm using Stack | Coding Interview Question

How to find out the largest area under Histogram in a Stack ? Learn to solve in this tutorial :)
For more online courses visit - http://www.codingblocks.com
Check courses on - http://online.codingblocks.com [FreeTrialAvailable]
Coding Blocks is pleased to announce courses like C++ and Java, Data Structures and Algorithms, Web and AndroidDevelopment(Java and Kotlin), Competitive Programming, Coding Interview Preparation and Machine Learning, AI and more.
#CodingBlocks #ProgrammingMadeEasy #LearnCodingOnline
Like our FaceBook Page - https://www.facebook.com/codingblocksindia/
Follow us on Instagram - https://www.instagram.com/codingblocks/
Follow us on Twitter - https://twitter.com/CodingBlocksIn
Source code available on -https://github.com/coding-blocks
For more interesting tu...

published: 17 Dec 2017

An efficient bandit algorithm for realtime multivariate optimization

An efficient bandit algorithm for realtime multivariate optimization
Daniel Hill (Amazon.com)
Houssam Nassif (Amazon.com)
Yi Liu (Amazon.com)
Anand Iyer (Amazon.com)
S. V. N. Vishwanathan (vishy@amazon.com)
Optimization is commonly employed to determine the content of web pages, such as to maximize conversions on landing pages or click-through rates on search engine result pages. Often the layout of these pages can be decoupled into several separate decisions. For example, the composition of a landing page may involve deciding which image to show, which wording to use, what color background to display, etc. Thus, optimization is a combinatorial problem over an exponentially large decision space. Randomized experiments do not scale well to this setting, and therefore, in practice, one is ...

Intro to Algorithms: Crash Course Computer Science #13

Algorithms are the sets of steps necessary to complete computation - they are at the heart of what our devices actually do. And this isn’t a new concept. Since ...

Algorithms are the sets of steps necessary to complete computation - they are at the heart of what our devices actually do. And this isn’t a new concept. Since the development of math itself algorithms have been needed to help us complete tasks more efficiently, but today we’re going to take a look a couple modern computing problems like sorting and graph search, and show how we’ve made them more efficient so you can more easily find cheap airfare or map directions to Winterfell... or like a restaurant or something.
Ps. Have you had the chance to play the Grace Hopper game we made in episode 12. Check it out here! http://thoughtcafe.ca/hopper/
CORRECTION:
In the pseudocode for selection sort at 3:09, this line:
swap array items at index and smallest
should be:
swap array items at i and smallest
Produced in collaboration with PBS Digital Studios: http://youtube.com/pbsdigitalstudios
Want to know more about Carrie Anne?
https://about.me/carrieannephilbin
The Latest from PBS Digital Studios: https://www.youtube.com/playlist?list...
Want to find Crash Course elsewhere on the internet?
Facebook - https://www.facebook.com/YouTubeCrash...
Twitter - http://www.twitter.com/TheCrashCourse
Tumblr - http://thecrashcourse.tumblr.com
Support Crash Course on Patreon: http://patreon.com/crashcourse
CC Kids: http://www.youtube.com/crashcoursekids

Algorithms are the sets of steps necessary to complete computation - they are at the heart of what our devices actually do. And this isn’t a new concept. Since the development of math itself algorithms have been needed to help us complete tasks more efficiently, but today we’re going to take a look a couple modern computing problems like sorting and graph search, and show how we’ve made them more efficient so you can more easily find cheap airfare or map directions to Winterfell... or like a restaurant or something.
Ps. Have you had the chance to play the Grace Hopper game we made in episode 12. Check it out here! http://thoughtcafe.ca/hopper/
CORRECTION:
In the pseudocode for selection sort at 3:09, this line:
swap array items at index and smallest
should be:
swap array items at i and smallest
Produced in collaboration with PBS Digital Studios: http://youtube.com/pbsdigitalstudios
Want to know more about Carrie Anne?
https://about.me/carrieannephilbin
The Latest from PBS Digital Studios: https://www.youtube.com/playlist?list...
Want to find Crash Course elsewhere on the internet?
Facebook - https://www.facebook.com/YouTubeCrash...
Twitter - http://www.twitter.com/TheCrashCourse
Tumblr - http://thecrashcourse.tumblr.com
Support Crash Course on Patreon: http://patreon.com/crashcourse
CC Kids: http://www.youtube.com/crashcoursekids

http://www.cppcon.org
—
Presentation Slides, PDFs, Source Code and other presenter materials are available at: https://github.com/CppCon/CppCon2014
--
Why do you write C++ code? There is a good chance it is in part because of concerns about the performance of your software. Whether they stem from needing to run on every smaller mobile devices, squeezing the last few effects into video game, or because every watt of power in your data center costs too much, C++ programmers throughout the industry have an insatiable desire for writing high performance code.
Unfortunately, even with C++, this can be really challenging. Over the past twenty years processors, memory, software libraries, and even compilers have radically changed what makes C++ code fast. Even measuring the performance of your code can be a daunting task. This talk will dig into how modern processors work, what makes them fast, and how to exploit them effectively with modern C++ code. It will teach you how modern C++ optimizers see your code today, and how that is likely to change in the coming years. It will teach you how to reason better about the performance of your code, and how to write your code so that it performs better. You will even learn some tricks about how to measure the performance of your code.
--
Chandler Carruth leads the Clang team at Google, building better diagnostics, tools, and more. Previously, he worked on several pieces of Google’s distributed build system. He makes guest appearances helping to maintain a few core C++ libraries across Google’s codebase, and is active in the LLVM and Clang open source communities. He received his M.S. and B.S. in Computer Science from Wake Forest University, but disavows all knowledge of the contents of his Master’s thesis. He is regularly found drinking Cherry CokeZero in the daytime and pontificating over a single malt scotch in the evening.
--
Videos Filmed & Edited by Bash Films: http://www.BashFilms.com

http://www.cppcon.org
—
Presentation Slides, PDFs, Source Code and other presenter materials are available at: https://github.com/CppCon/CppCon2014
--
Why do you write C++ code? There is a good chance it is in part because of concerns about the performance of your software. Whether they stem from needing to run on every smaller mobile devices, squeezing the last few effects into video game, or because every watt of power in your data center costs too much, C++ programmers throughout the industry have an insatiable desire for writing high performance code.
Unfortunately, even with C++, this can be really challenging. Over the past twenty years processors, memory, software libraries, and even compilers have radically changed what makes C++ code fast. Even measuring the performance of your code can be a daunting task. This talk will dig into how modern processors work, what makes them fast, and how to exploit them effectively with modern C++ code. It will teach you how modern C++ optimizers see your code today, and how that is likely to change in the coming years. It will teach you how to reason better about the performance of your code, and how to write your code so that it performs better. You will even learn some tricks about how to measure the performance of your code.
--
Chandler Carruth leads the Clang team at Google, building better diagnostics, tools, and more. Previously, he worked on several pieces of Google’s distributed build system. He makes guest appearances helping to maintain a few core C++ libraries across Google’s codebase, and is active in the LLVM and Clang open source communities. He received his M.S. and B.S. in Computer Science from Wake Forest University, but disavows all knowledge of the contents of his Master’s thesis. He is regularly found drinking Cherry CokeZero in the daytime and pontificating over a single malt scotch in the evening.
--
Videos Filmed & Edited by Bash Films: http://www.BashFilms.com

Fastest Sorting Algorithm. Ever!

What is the fastest sorting algorithm known to man? We find out by attempting to find an O(n) time complexity sorting algorithm. Till now, the research on sorti...

What is the fastest sorting algorithm known to man? We find out by attempting to find an O(n) time complexity sorting algorithm. Till now, the research on sorting has found O(n*log(n)) to be the best.
We discuss concepts of time complexity, decision making and how the best algorithm is bounded by the permutation tree of an array. A final trick is used to bring down the complexity to O(N)!
N! Approximation: https://en.wikipedia.org/wiki/Stirling%27s_approximation
log(N) video: https://www.youtube.com/watch?v=Xe9aq1WLpjU&vl=en
Sources:
https://hackernoon.com/timsort-the-fastest-sorting-algorithm-youve-never-heard-of-36b28417f399
https://stackoverflow.com/questions/3969813/which-parallel-sorting-algorithm-has-the-best-average-case-performance
https://stackoverflow.com/questions/2352313/is-there-an-on-integer-sorting-algorithm
#sorting #algorithm #sortingalgorithm

What is the fastest sorting algorithm known to man? We find out by attempting to find an O(n) time complexity sorting algorithm. Till now, the research on sorting has found O(n*log(n)) to be the best.
We discuss concepts of time complexity, decision making and how the best algorithm is bounded by the permutation tree of an array. A final trick is used to bring down the complexity to O(N)!
N! Approximation: https://en.wikipedia.org/wiki/Stirling%27s_approximation
log(N) video: https://www.youtube.com/watch?v=Xe9aq1WLpjU&vl=en
Sources:
https://hackernoon.com/timsort-the-fastest-sorting-algorithm-youve-never-heard-of-36b28417f399
https://stackoverflow.com/questions/3969813/which-parallel-sorting-algorithm-has-the-best-average-case-performance
https://stackoverflow.com/questions/2352313/is-there-an-on-integer-sorting-algorithm
#sorting #algorithm #sortingalgorithm

Count Min sketch is a simple technique to summarize large amounts of frequency data. which is widely used in many places where there is a streaming big data.
D...

Count Min sketch is a simple technique to summarize large amounts of frequency data. which is widely used in many places where there is a streaming big data.
Donate/Patreon: https://www.patreon.com/techdummies
CODE:
----------------------------------------------------------------------------
By Varun Vats: https://gist.github.com/VarunVats9/7f379199d7658b96d479ee3c945f1b4a
Applications of count min sketch:
----------------------------------------------------------------------------
http://theory.stanford.edu/~tim/s15/l/l2.pdf
http://highscalability.com/blog/2016/1/25/design-of-a-modern-cache.html
https://spark.apache.org/docs/2.0.1/api/java/org/apache/spark/util/sketch/CountMinSketch.html
Applications using CountTracking There are dozens of applications of count tracking and in particular, the Count-Min sketch datastructure that goes beyond the task of approximating data distributions. We give three examples.
1. A more general query is to identify the Heavy-Hitters, that is, the query HH(k) returns theset of items which have large frequency (say 1/k of the overall frequency). Count trackingcan be used to directly answer this query, by considering the frequency of each item. Whenthere are very many possible items, answering the query in this way can be quite slow. Theprocess can be sped up immensely by keeping additional information about the frequenciesof groups of items [6], at the expense of storing additional sketches. As well as being ofinterest in mining applications, finding heavy-hitters is also of interest in the context of signalprocessing. Here, viewing the signal as defining a data distribution, recovering the heavy-hitters is key to building the best approximation of the signal. As a result, the Count-Minsketch can be used in compressed sensing, a signal acquisition paradigm that has recentlyrevolutionized signal processing [7].
2. One application where very large data sets arise is in Natural Language Processing (NLP).Here, it is important to keep statistics on the frequency of word combinations, such as pairsor triplets of words that occur in sequence. In one experiment, researchers compacted a large6
Page 7
90GB corpus down to a (memory friendly) 8GB Count-Min sketch [8]. This proved to be justas effective for their word similarity tasks as using the exact data.
3. A third example is in designing a mechanism to help users pick a safe password. To makepassword guessing difficult, we can track the frequency of passwords online and disallowcurrently popular ones. This is precisely the count tracking problem. Recently, this wasput into practice using the Count-Min data structure to do count tracking (see http://www.youtube.com/watch?v=qo1cOJFEF0U). A nice feature of this solution is that the impactof a false positive—erroneously declaring a rare password choice to be too popular and sodisallowing it—is only a mild inconvenience to the user

Count Min sketch is a simple technique to summarize large amounts of frequency data. which is widely used in many places where there is a streaming big data.
Donate/Patreon: https://www.patreon.com/techdummies
CODE:
----------------------------------------------------------------------------
By Varun Vats: https://gist.github.com/VarunVats9/7f379199d7658b96d479ee3c945f1b4a
Applications of count min sketch:
----------------------------------------------------------------------------
http://theory.stanford.edu/~tim/s15/l/l2.pdf
http://highscalability.com/blog/2016/1/25/design-of-a-modern-cache.html
https://spark.apache.org/docs/2.0.1/api/java/org/apache/spark/util/sketch/CountMinSketch.html
Applications using CountTracking There are dozens of applications of count tracking and in particular, the Count-Min sketch datastructure that goes beyond the task of approximating data distributions. We give three examples.
1. A more general query is to identify the Heavy-Hitters, that is, the query HH(k) returns theset of items which have large frequency (say 1/k of the overall frequency). Count trackingcan be used to directly answer this query, by considering the frequency of each item. Whenthere are very many possible items, answering the query in this way can be quite slow. Theprocess can be sped up immensely by keeping additional information about the frequenciesof groups of items [6], at the expense of storing additional sketches. As well as being ofinterest in mining applications, finding heavy-hitters is also of interest in the context of signalprocessing. Here, viewing the signal as defining a data distribution, recovering the heavy-hitters is key to building the best approximation of the signal. As a result, the Count-Minsketch can be used in compressed sensing, a signal acquisition paradigm that has recentlyrevolutionized signal processing [7].
2. One application where very large data sets arise is in Natural Language Processing (NLP).Here, it is important to keep statistics on the frequency of word combinations, such as pairsor triplets of words that occur in sequence. In one experiment, researchers compacted a large6
Page 7
90GB corpus down to a (memory friendly) 8GB Count-Min sketch [8]. This proved to be justas effective for their word similarity tasks as using the exact data.
3. A third example is in designing a mechanism to help users pick a safe password. To makepassword guessing difficult, we can track the frequency of passwords online and disallowcurrently popular ones. This is precisely the count tracking problem. Recently, this wasput into practice using the Count-Min data structure to do count tracking (see http://www.youtube.com/watch?v=qo1cOJFEF0U). A nice feature of this solution is that the impactof a false positive—erroneously declaring a rare password choice to be too popular and sodisallowing it—is only a mild inconvenience to the user

Big O Notation

Learn about Big O notation, an equation that describes how the run time scales with respect to some input variables. This video is a part of HackerRank's Cracki...

Learn about Big O notation, an equation that describes how the run time scales with respect to some input variables. This video is a part of HackerRank's Cracking The Coding Interview Tutorial with Gayle Laakmann McDowell. http://www.hackerrank.com/domains/tutorials/cracking-the-coding-interview?utm_source=video&utm_medium=youtube&utm_campaign=ctci

Learn about Big O notation, an equation that describes how the run time scales with respect to some input variables. This video is a part of HackerRank's Cracking The Coding Interview Tutorial with Gayle Laakmann McDowell. http://www.hackerrank.com/domains/tutorials/cracking-the-coding-interview?utm_source=video&utm_medium=youtube&utm_campaign=ctci

C Programming Tutorial #26 What is an algorithm? Fast arithmetic, Correctness and efficiency, Repeated squaring method. This lecture will introduce the notion of an algorithm and introduce the ideas of correctness and efficiency. The running example is the problem of raising a number to a power, i.e. computing AN for numbers A and N. Two different methods will be discussed and compared. You will see that both methods are correct, but one method is superior to or more efficient than the other. You will learn to quantify the efficiency of the two methods exactly and compare them.
The lecture contains the following topics:
00:20 What is an algorithm?
03:30 Fast arithmetic: Raise a number to a power
10:20 Correctness and efficiency
12:09 Repeated squaring method
C Programming Tutorial/ Lectures/ Course with Data Structures & Algorithms @ https://goo.gl/K6VbgL
Machine LearningComplete Tutorial/Lectures/Course from IIT (nptel) for GATE @ https://goo.gl/AurRXm
Discrete Mathematics for Computer Science for GATE @ https://goo.gl/YJnA4B (IIT Lectures for GATE)
Best Programming Courses @ https://goo.gl/MVVDXR
Operating Systems Lecture/Tutorials from IIT for GATE CSE @ https://goo.gl/GMr3if
MATLAB Tutorials for GATE @ https://goo.gl/EiPgCF

C Programming Tutorial #26 What is an algorithm? Fast arithmetic, Correctness and efficiency, Repeated squaring method. This lecture will introduce the notion of an algorithm and introduce the ideas of correctness and efficiency. The running example is the problem of raising a number to a power, i.e. computing AN for numbers A and N. Two different methods will be discussed and compared. You will see that both methods are correct, but one method is superior to or more efficient than the other. You will learn to quantify the efficiency of the two methods exactly and compare them.
The lecture contains the following topics:
00:20 What is an algorithm?
03:30 Fast arithmetic: Raise a number to a power
10:20 Correctness and efficiency
12:09 Repeated squaring method
C Programming Tutorial/ Lectures/ Course with Data Structures & Algorithms @ https://goo.gl/K6VbgL
Machine LearningComplete Tutorial/Lectures/Course from IIT (nptel) for GATE @ https://goo.gl/AurRXm
Discrete Mathematics for Computer Science for GATE @ https://goo.gl/YJnA4B (IIT Lectures for GATE)
Best Programming Courses @ https://goo.gl/MVVDXR
Operating Systems Lecture/Tutorials from IIT for GATE CSE @ https://goo.gl/GMr3if
MATLAB Tutorials for GATE @ https://goo.gl/EiPgCF

How to Write Algorithm
How to AnalyseSimple Algorithm.
Criteria for Analysing Algorithm
Buy C++ course on Udemy.com
Price: $10.99 (₹750)
URL : https://www.udemy.com/cpp-deep-dive/?couponCode=LEARNCPP
Course covers All the topics from Basics to Advance level.
Every topic is covered in greater detail with suitable examples.
Suitable for Academics and Industry

How to Write Algorithm
How to AnalyseSimple Algorithm.
Criteria for Analysing Algorithm
Buy C++ course on Udemy.com
Price: $10.99 (₹750)
URL : https://www.udemy.com/cpp-deep-dive/?couponCode=LEARNCPP
Course covers All the topics from Basics to Advance level.
Every topic is covered in greater detail with suitable examples.
Suitable for Academics and Industry

Largest Area under Histogram efficient algorithm using Stack | Coding Interview Question

How to find out the largest area under Histogram in a Stack ? Learn to solve in this tutorial :)
For more online courses visit - http://www.codingblocks.com
C...

How to find out the largest area under Histogram in a Stack ? Learn to solve in this tutorial :)
For more online courses visit - http://www.codingblocks.com
Check courses on - http://online.codingblocks.com [FreeTrialAvailable]
Coding Blocks is pleased to announce courses like C++ and Java, Data Structures and Algorithms, Web and AndroidDevelopment(Java and Kotlin), Competitive Programming, Coding Interview Preparation and Machine Learning, AI and more.
#CodingBlocks #ProgrammingMadeEasy #LearnCodingOnline
Like our FaceBook Page - https://www.facebook.com/codingblocksindia/
Follow us on Instagram - https://www.instagram.com/codingblocks/
Follow us on Twitter - https://twitter.com/CodingBlocksIn
Source code available on -https://github.com/coding-blocks
For more interesting tutorials - https://www.youtube.com/channel/UCICWIYEx2mo4wYZzLwJ7wVw

How to find out the largest area under Histogram in a Stack ? Learn to solve in this tutorial :)
For more online courses visit - http://www.codingblocks.com
Check courses on - http://online.codingblocks.com [FreeTrialAvailable]
Coding Blocks is pleased to announce courses like C++ and Java, Data Structures and Algorithms, Web and AndroidDevelopment(Java and Kotlin), Competitive Programming, Coding Interview Preparation and Machine Learning, AI and more.
#CodingBlocks #ProgrammingMadeEasy #LearnCodingOnline
Like our FaceBook Page - https://www.facebook.com/codingblocksindia/
Follow us on Instagram - https://www.instagram.com/codingblocks/
Follow us on Twitter - https://twitter.com/CodingBlocksIn
Source code available on -https://github.com/coding-blocks
For more interesting tutorials - https://www.youtube.com/channel/UCICWIYEx2mo4wYZzLwJ7wVw

An efficient bandit algorithm for realtime multivariate optimization
Daniel Hill (Amazon.com)
Houssam Nassif (Amazon.com)
Yi Liu (Amazon.com)
Anand Iyer (Amazon.com)
S. V. N. Vishwanathan (vishy@amazon.com)
Optimization is commonly employed to determine the content of web pages, such as to maximize conversions on landing pages or click-through rates on search engine result pages. Often the layout of these pages can be decoupled into several separate decisions. For example, the composition of a landing page may involve deciding which image to show, which wording to use, what color background to display, etc. Thus, optimization is a combinatorial problem over an exponentially large decision space. Randomized experiments do not scale well to this setting, and therefore, in practice, one is typically limited to optimizing a single aspect of a web page at a time. This represents a missed opportunity in both the speed of experimentation and the exploitation of possible interactions between layout decisions.
Here we focus on multivariate optimization of interactive web pages. We formulate an approach where the possible interactions between different components of the page are modeled explicitly. We apply bandit methodology to explore the layout space efficiently and use hill-climbing to select optimal content in realtime. Our algorithm also extends to contextualization and personalization of layout selection. Simulation results show the suitability of our approach to large decision spaces with strong interactions between content. We further apply our algorithm to optimize a message that promotes adoption of an Amazon service. After only a single week of online optimization, we saw a 21\% increase in purchase rate compared to the average layout. Our technique is currently being deployed to optimize content across several locations at Amazon.com.
More on http://www.kdd.org/kdd2017/

An efficient bandit algorithm for realtime multivariate optimization
Daniel Hill (Amazon.com)
Houssam Nassif (Amazon.com)
Yi Liu (Amazon.com)
Anand Iyer (Amazon.com)
S. V. N. Vishwanathan (vishy@amazon.com)
Optimization is commonly employed to determine the content of web pages, such as to maximize conversions on landing pages or click-through rates on search engine result pages. Often the layout of these pages can be decoupled into several separate decisions. For example, the composition of a landing page may involve deciding which image to show, which wording to use, what color background to display, etc. Thus, optimization is a combinatorial problem over an exponentially large decision space. Randomized experiments do not scale well to this setting, and therefore, in practice, one is typically limited to optimizing a single aspect of a web page at a time. This represents a missed opportunity in both the speed of experimentation and the exploitation of possible interactions between layout decisions.
Here we focus on multivariate optimization of interactive web pages. We formulate an approach where the possible interactions between different components of the page are modeled explicitly. We apply bandit methodology to explore the layout space efficiently and use hill-climbing to select optimal content in realtime. Our algorithm also extends to contextualization and personalization of layout selection. Simulation results show the suitability of our approach to large decision spaces with strong interactions between content. We further apply our algorithm to optimize a message that promotes adoption of an Amazon service. After only a single week of online optimization, we saw a 21\% increase in purchase rate compared to the average layout. Our technique is currently being deployed to optimize content across several locations at Amazon.com.
More on http://www.kdd.org/kdd2017/

Intro to Algorithms: Crash Course Computer Science #13

Algorithms are the sets of steps necessary to complete computation - they are at the heart of what our devices actually do. And this isn’t a new concept. Since the development of math itself algorithms have been needed to help us complete tasks more efficiently, but today we’re going to take a look a couple modern computing problems like sorting and graph search, and show how we’ve made them more efficient so you can more easily find cheap airfare or map directions to Winterfell... or like a restaurant or something.
Ps. Have you had the chance to play the Grace Hopper game we made in episode 12. Check it out here! http://thoughtcafe.ca/hopper/
CORRECTION:
In the pseudocode for selection sort at 3:09, this line:
swap array items at index and smallest
should be:
swap array items at i and smallest
Produced in collaboration with PBS Digital Studios: http://youtube.com/pbsdigitalstudios
Want to know more about Carrie Anne?
https://about.me/carrieannephilbin
The Latest from PBS Digital Studios: https://www.youtube.com/playlist?list...
Want to find Crash Course elsewhere on the internet?
Facebook - https://www.facebook.com/YouTubeCrash...
Twitter - http://www.twitter.com/TheCrashCourse
Tumblr - http://thecrashcourse.tumblr.com
Support Crash Course on Patreon: http://patreon.com/crashcourse
CC Kids: http://www.youtube.com/crashcoursekids

http://www.cppcon.org
—
Presentation Slides, PDFs, Source Code and other presenter materials are available at: https://github.com/CppCon/CppCon2014
--
Why do you write C++ code? There is a good chance it is in part because of concerns about the performance of your software. Whether they stem from needing to run on every smaller mobile devices, squeezing the last few effects into video game, or because every watt of power in your data center costs too much, C++ programmers throughout the industry have an insatiable desire for writing high performance code.
Unfortunately, even with C++, this can be really challenging. Over the past twenty years processors, memory, software libraries, and even compilers have radically changed what makes C++ code fast. Even measuring the performance of your code can be a daunting task. This talk will dig into how modern processors work, what makes them fast, and how to exploit them effectively with modern C++ code. It will teach you how modern C++ optimizers see your code today, and how that is likely to change in the coming years. It will teach you how to reason better about the performance of your code, and how to write your code so that it performs better. You will even learn some tricks about how to measure the performance of your code.
--
Chandler Carruth leads the Clang team at Google, building better diagnostics, tools, and more. Previously, he worked on several pieces of Google’s distributed build system. He makes guest appearances helping to maintain a few core C++ libraries across Google’s codebase, and is active in the LLVM and Clang open source communities. He received his M.S. and B.S. in Computer Science from Wake Forest University, but disavows all knowledge of the contents of his Master’s thesis. He is regularly found drinking Cherry CokeZero in the daytime and pontificating over a single malt scotch in the evening.
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Videos Filmed & Edited by Bash Films: http://www.BashFilms.com

Fastest Sorting Algorithm. Ever!

What is the fastest sorting algorithm known to man? We find out by attempting to find an O(n) time complexity sorting algorithm. Till now, the research on sorting has found O(n*log(n)) to be the best.
We discuss concepts of time complexity, decision making and how the best algorithm is bounded by the permutation tree of an array. A final trick is used to bring down the complexity to O(N)!
N! Approximation: https://en.wikipedia.org/wiki/Stirling%27s_approximation
log(N) video: https://www.youtube.com/watch?v=Xe9aq1WLpjU&vl=en
Sources:
https://hackernoon.com/timsort-the-fastest-sorting-algorithm-youve-never-heard-of-36b28417f399
https://stackoverflow.com/questions/3969813/which-parallel-sorting-algorithm-has-the-best-average-case-performance
https://stackoverflow.com/questions/2352313/is-there-an-on-integer-sorting-algorithm
#sorting #algorithm #sortingalgorithm

Count Min sketch is a simple technique to summarize large amounts of frequency data. which is widely used in many places where there is a streaming big data.
Donate/Patreon: https://www.patreon.com/techdummies
CODE:
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By Varun Vats: https://gist.github.com/VarunVats9/7f379199d7658b96d479ee3c945f1b4a
Applications of count min sketch:
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http://theory.stanford.edu/~tim/s15/l/l2.pdf
http://highscalability.com/blog/2016/1/25/design-of-a-modern-cache.html
https://spark.apache.org/docs/2.0.1/api/java/org/apache/spark/util/sketch/CountMinSketch.html
Applications using CountTracking There are dozens of applications of count tracking and in particular, the Count-Min sketch datastructure that goes beyond the task of approximating data distributions. We give three examples.
1. A more general query is to identify the Heavy-Hitters, that is, the query HH(k) returns theset of items which have large frequency (say 1/k of the overall frequency). Count trackingcan be used to directly answer this query, by considering the frequency of each item. Whenthere are very many possible items, answering the query in this way can be quite slow. Theprocess can be sped up immensely by keeping additional information about the frequenciesof groups of items [6], at the expense of storing additional sketches. As well as being ofinterest in mining applications, finding heavy-hitters is also of interest in the context of signalprocessing. Here, viewing the signal as defining a data distribution, recovering the heavy-hitters is key to building the best approximation of the signal. As a result, the Count-Minsketch can be used in compressed sensing, a signal acquisition paradigm that has recentlyrevolutionized signal processing [7].
2. One application where very large data sets arise is in Natural Language Processing (NLP).Here, it is important to keep statistics on the frequency of word combinations, such as pairsor triplets of words that occur in sequence. In one experiment, researchers compacted a large6
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90GB corpus down to a (memory friendly) 8GB Count-Min sketch [8]. This proved to be justas effective for their word similarity tasks as using the exact data.
3. A third example is in designing a mechanism to help users pick a safe password. To makepassword guessing difficult, we can track the frequency of passwords online and disallowcurrently popular ones. This is precisely the count tracking problem. Recently, this wasput into practice using the Count-Min data structure to do count tracking (see http://www.youtube.com/watch?v=qo1cOJFEF0U). A nice feature of this solution is that the impactof a false positive—erroneously declaring a rare password choice to be too popular and sodisallowing it—is only a mild inconvenience to the user

Big O Notation

Learn about Big O notation, an equation that describes how the run time scales with respect to some input variables. This video is a part of HackerRank's Cracking The Coding Interview Tutorial with Gayle Laakmann McDowell. http://www.hackerrank.com/domains/tutorials/cracking-the-coding-interview?utm_source=video&utm_medium=youtube&utm_campaign=ctci

#26 What is an algorithm? Its Correctness and efficiency

C Programming Tutorial #26 What is an algorithm? Fast arithmetic, Correctness and efficiency, Repeated squaring method. This lecture will introduce the notion of an algorithm and introduce the ideas of correctness and efficiency. The running example is the problem of raising a number to a power, i.e. computing AN for numbers A and N. Two different methods will be discussed and compared. You will see that both methods are correct, but one method is superior to or more efficient than the other. You will learn to quantify the efficiency of the two methods exactly and compare them.
The lecture contains the following topics:
00:20 What is an algorithm?
03:30 Fast arithmetic: Raise a number to a power
10:20 Correctness and efficiency
12:09 Repeated squaring method
C Programming Tutorial/ Lectures/ Course with Data Structures & Algorithms @ https://goo.gl/K6VbgL
Machine LearningComplete Tutorial/Lectures/Course from IIT (nptel) for GATE @ https://goo.gl/AurRXm
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Operating Systems Lecture/Tutorials from IIT for GATE CSE @ https://goo.gl/GMr3if
MATLAB Tutorials for GATE @ https://goo.gl/EiPgCF

1.3 How Write and Analyze Algorithm

How to Write Algorithm
How to AnalyseSimple Algorithm.
Criteria for Analysing Algorithm
Buy C++ course on Udemy.com
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Course covers All the topics from Basics to Advance level.
Every topic is covered in greater detail with suitable examples.
Suitable for Academics and Industry

Largest Area under Histogram efficient algorithm using Stack | Coding Interview Question

How to find out the largest area under Histogram in a Stack ? Learn to solve in this tutorial :)
For more online courses visit - http://www.codingblocks.com
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Coding Blocks is pleased to announce courses like C++ and Java, Data Structures and Algorithms, Web and AndroidDevelopment(Java and Kotlin), Competitive Programming, Coding Interview Preparation and Machine Learning, AI and more.
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An efficient bandit algorithm for realtime multivariate optimization

An efficient bandit algorithm for realtime multivariate optimization
Daniel Hill (Amazon.com)
Houssam Nassif (Amazon.com)
Yi Liu (Amazon.com)
Anand Iyer (Amazon.com)
S. V. N. Vishwanathan (vishy@amazon.com)
Optimization is commonly employed to determine the content of web pages, such as to maximize conversions on landing pages or click-through rates on search engine result pages. Often the layout of these pages can be decoupled into several separate decisions. For example, the composition of a landing page may involve deciding which image to show, which wording to use, what color background to display, etc. Thus, optimization is a combinatorial problem over an exponentially large decision space. Randomized experiments do not scale well to this setting, and therefore, in practice, one is typically limited to optimizing a single aspect of a web page at a time. This represents a missed opportunity in both the speed of experimentation and the exploitation of possible interactions between layout decisions.
Here we focus on multivariate optimization of interactive web pages. We formulate an approach where the possible interactions between different components of the page are modeled explicitly. We apply bandit methodology to explore the layout space efficiently and use hill-climbing to select optimal content in realtime. Our algorithm also extends to contextualization and personalization of layout selection. Simulation results show the suitability of our approach to large decision spaces with strong interactions between content. We further apply our algorithm to optimize a message that promotes adoption of an Amazon service. After only a single week of online optimization, we saw a 21\% increase in purchase rate compared to the average layout. Our technique is currently being deployed to optimize content across several locations at Amazon.com.
More on http://www.kdd.org/kdd2017/